Calling relevant libraries
#READING THE DATA-SET
names(candy_data_all_years)
[1] "year" "age" "trick_or_treat_yourself" "candy_name"
[5] "emotion" "gender" "country"
Analysis questions
1> What is the total number of candy ratings given across the three years. (number of candy ratings, not number of raters. Don’t count missing values)
candy_data_all_years %>%
filter(!is.na(emotion)) %>%
summarise(total_no_of_candy_ratings = n())
2> What was the average age of people who are going out trick or treating and the average age of people 3. not going trick or treating?
candy_data_all_years %>%
group_by(trick_or_treat_yourself) %>%
summarise( average_age = mean(age,na.rm = TRUE))
NA
3> For each of joy, despair and meh, which candy bar revived the most of these ratings?
candy_data_all_years %>%
filter(!is.na(emotion))%>%
group_by(emotion, candy_name) %>%
summarise( count = n()) %>%
filter(count == max(count))
4> How many people rated Starburst as despair?
candy_data_all_years %>%
filter(candy_name == "starburst", emotion == "DESPAIR") %>%
summarise (total_starburst_despair = n())
For the next three questions, count despair as -1, joy as +1 and meh as 0.
5> What was the most popular candy bar by this rating system for each gender in the dataset?
6> What was the most popular candy bar in each year?
7> What was the most popular candy bar by this rating for people in US, Canada, UK and all other countries?
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